Abstract
Optimal sensor placement (OSP) is an important task during the implementation of sophisticated structural health monitoring (SHM) systems for large-scale structures. In this paper, a comparative study between the genetic algorithm (GA) and the firefly algorithm (FA) in solving the OSP problem is conducted. To overcome the drawback related to the inapplicability of the FA in optimization problems with discrete variables, some improvements are proposed, including the one-dimensional binary coding system, the Hamming distance between any two fireflies, and the semioriented movement scheme; also, a simple discrete firefly algorithm (SDFA) is developed. The capabilities of the SDFA and the GA in finding the optimal sensor locations are evaluated using two disparate objective functions in a numerical example with a long-span benchmark cable-stayed bridge. The results show that the developed SDFA can find the optimal sensor configuration with high reliability. The comparative study indicates that the SDFA outperforms the GA in terms of algorithm complexity, computational efficiency, and result quality. The optimization mechanism of the FA has the potential to be extended to a wide range of optimization problems.
Highlights
The performance deterioration and the total collapse of largescale civil infrastructures induced by the environment and service loads highlight the importance of structural health monitoring (SHM) as a significant approach for the safe operation and the reasonable maintenance of structures
SHM, which involves an array of sensors to continuously monitor structural behavior, along with the extraction of damage-sensitive features from these measurements and the evaluation of current system health by analysis methods, can be used for rapid condition screening and aims to provide reliable information regarding the integrity of the structure in near real time [1,2,3]
A comparative study is conducted between the simple discrete firefly algorithm (SDFA) and the genetic algorithm (GA), in terms of computational efficiency and result quality
Summary
The performance deterioration and the total collapse of largescale civil infrastructures induced by the environment and service loads highlight the importance of structural health monitoring (SHM) as a significant approach for the safe operation and the reasonable maintenance of structures. Conventional gradient-based local optimization methods were unable to efficiently handle multiple local optima and may present difficulties in estimating the global minimum. They lack reliability in dealing with the OSP problem, because convergence to the global minimum is. Some improvements, including the coding system, the suitable distance, and the movement scheme, are introduced, and a simple discrete firefly algorithm (SDFA) is proposed based on the FA such that the outstanding optimization mechanism of the FA can be applicable in the OSP problem with discrete variables.
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